library(wigglescout)
library(ggplot2)
RStudio Community is a great place to get help: https://community.rstudio.com/c/tidyverse
library(reshape2)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(ggpubr)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
library(GenomicRanges)
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
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first, rename
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expand.grid
Loading required package: IRanges
Attaching package: ‘IRanges’
The following objects are masked from ‘package:dplyr’:
collapse, desc, slice
Loading required package: GenomeInfoDb
source('../lib/lib_embed_data.v1.0.R')
Loading required package: xfun
Attaching package: ‘xfun’
The following objects are masked from ‘package:base’:
attr, isFALSE
data_path <- '/Volumes/DATA/DATA/GLOE/GLOE/replicates/'
replicates <- c("WT-UT-1-1","WT-UT-1-2","WT-UT-1-3","WT-20-2-1","WT-20-2-2","WT-20-2-3","WT-24hr-3-1","WT-24hr-3-2","WT-24hr-3-3","KO-UT-4-1","KO-UT-4-2","KO-UT-4-3","KO-20-5-1","KO-20-5-2","KO-20-5-3","KO-24hr-6-1","KO-24hr-6-2","KO-24hr-6-3")
samples <- c("WT-UT-1","WT-20-2","WT-24hr-3","KO-UT-4","KO-20-5","KO-24hr-6")
build_filenames <- function(path, cond) {
files <- paste0(path,"/GL-",cond,".hg19.bs50.bw")
labels <- paste0("GLOE ", cond)
return(data.frame(files=files,labels=labels))
}
build_filenames_plus <- function(path, cond) {
files <- paste0(path,"/GL-",cond,".hg19.R1plus.bw")
labels <- paste0("GLOE (+) ", cond)
return(data.frame(files=files,labels=labels))
}
build_filenames_minus <- function(path, cond) {
files <- paste0(path,"//GL-",cond,".hg19.R1minus.bw")
labels <- paste0("GLOE (-) ", cond)
return(data.frame(files=files,labels=labels))
}
Published peaksets (Cistrome)
Here, we are comparing profiles accross known accessible, DNAase hypersensitive regions, and CTCF peaks Note: unlike in previous plots, we clearly see now a correlation of CAD activity and accessible chromatin. Plotting average profiles on scarce read data can be thrown of by artifact regions. These artifacts are avoided here by 1) using a blacklist to remove known repetitive/artifact-prone regions and 2) removing outliers (top 0.1%) of the signal before averaging.
fn <- build_filenames(data_path,samples)
p1 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_ATACseq.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)
p2 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)
p3 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)
ggarrange(p1, p2, p3, ncol = 3, nrow = 1)

Pileup regions = ‘hypersensitive breakpoints’
Given the overall low coverage/incidence of SSB in the genome, a coincidence of several SSBs in the same location (or even exact same base pair) is highly unlikely to occur by chance. Noticing that around CTCF sites, uniqe GLOE-Seq reads piled up neatly (some with exact same starting base), I developed a pipeline that looks for such pileups. There are ~40 of these regions identified, which I would call ‘hypersensitive’. There may be many more but it is not possible to call them confidently.
fn <- build_filenames(data_path,samples)
p1 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))
p3 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.shuffled.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))
fn <- build_filenames(data_path,replicates[c(7,8,9,16,17,18)])
p2 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))
embed_plot(p1,"CAD-peaks")
Saving 7 x 7 in image
Download CAD-peaks.pdf
embed_plot(p1,"CAD-peaks.rep")
Download CAD-peaks.rep.pdf
embed_plot(p1,"CAD-ctrl")
Download CAD-ctrl.pdf
ggarrange(p1, p2, p3, ncol = 3, nrow = 1)

Heatmaps of the same regions
fn <- build_filenames(data_path,samples)
p1 <- plot_bw_heatmap(bwfile = fn$files[1], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p2 <- plot_bw_heatmap(bwfile = fn$files[2], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p3 <- plot_bw_heatmap(bwfile = fn$files[3], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p4 <- plot_bw_heatmap(bwfile = fn$files[4], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p5 <- plot_bw_heatmap(bwfile = fn$files[5], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p6 <- plot_bw_heatmap(bwfile = fn$files[6], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
ggarrange(p1, p2, p3, p4, p5, p6, ncol = 6, nrow = 1)

CTCF sites - spacing relative to nucleosomes and linker histone
around CTCF sites, the GLOE-seq signal looked very periodic, which suggested that there could be a relationship to nucleosome occupancy. Indeed, the pattern is consistent with CAD cutting left and right of linker histone / in-between linker histone and nucleosome. Note that the same periodicity is also seen in untreated and CAD KO conditions, albeit much less pronounced, suggesting that CAD-independent SSBs are also occuring more often in-between nucleosomes.

DNase hypersensitive sites - spacing relative to nucleosomes and linker histone
With the removal of artifactual regions, we now also can see a neatly spaced pattern at DNAse hypersensitive sites.
p4<- plot_bw_profile(bwfiles = c(plus$files[3],minus$files[3]),labels = c("WT 24h +","WT 24h -"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)
p4<- plot_bw_profile(bwfiles = c(plus$files[3],minus$files[3]),labels = c("WT 24h +","WT 24h -"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)
p5 <- plot_bw_profile(bwfiles = plus$files[c(1,3,6)],labels = plus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)
p6 <- plot_bw_profile(bwfiles = minus$files[c(1,3,6)],labels = minus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)
ggarrange(p1, p2, p3, p4, p5, p6, ncol = 3, nrow = 2)

---
title: "GLOE-Seq analysis"
output: html_notebook
---

```{r warning=FALSE}

library(wigglescout)
library(ggplot2)
library(reshape2)
library(dplyr)
library(ggpubr)
library(GenomicRanges)


source('../lib/lib_embed_data.v1.0.R')

data_path <- '/Volumes/DATA/DATA/GLOE/GLOE/replicates/'

replicates <- c("WT-UT-1-1","WT-UT-1-2","WT-UT-1-3","WT-20-2-1","WT-20-2-2","WT-20-2-3","WT-24hr-3-1","WT-24hr-3-2","WT-24hr-3-3","KO-UT-4-1","KO-UT-4-2","KO-UT-4-3","KO-20-5-1","KO-20-5-2","KO-20-5-3","KO-24hr-6-1","KO-24hr-6-2","KO-24hr-6-3")
samples <- c("WT-UT-1","WT-20-2","WT-24hr-3","KO-UT-4","KO-20-5","KO-24hr-6")

build_filenames <- function(path, cond) {
  files <- paste0(path,"/GL-",cond,".hg19.bs50.bw")
  labels <- paste0("GLOE ", cond)
  return(data.frame(files=files,labels=labels))
}

build_filenames_plus <- function(path, cond) {
  files <- paste0(path,"/GL-",cond,".hg19.R1plus.bw")
  labels <- paste0("GLOE (+) ", cond)
  return(data.frame(files=files,labels=labels))
}

build_filenames_minus <- function(path, cond) {
  files <- paste0(path,"//GL-",cond,".hg19.R1minus.bw")
  labels <- paste0("GLOE (-) ", cond)
  return(data.frame(files=files,labels=labels))
}

```

## Published peaksets (Cistrome)

Here, we are comparing profiles accross known accessible, DNAase hypersensitive regions, and CTCF peaks
*Note: unlike in previous plots, we clearly see now a correlation of CAD activity and accessible chromatin. Plotting average profiles on scarce read data can be thrown of by artifact regions. These artifacts are avoided here by 1) using a blacklist to remove known repetitive/artifact-prone regions and 2) removing outliers (top 0.1%) of the signal before averaging.*

```{r fig.width = 8, fig.height = 2}
fn <- build_filenames(data_path,samples)

p1 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_ATACseq.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)
p2 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)
p3 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10 ,remove_top = 0.001, verbose=F)

ggarrange(p1, p2, p3, ncol = 3, nrow = 1)
```

## Pileup regions = 'hypersensitive breakpoints'

Given the overall low coverage/incidence of SSB in the genome, a coincidence of several SSBs in the same location (or even exact same base pair) is highly unlikely to occur by chance. Noticing that around CTCF sites, uniqe GLOE-Seq reads piled up neatly (some with exact same starting base), I developed a pipeline that looks for such pileups. There are ~40 of these regions identified, which I would call 'hypersensitive'. There may be many more but it is not possible to call them confidently.

```{r fig.width = 8, fig.height = 2}

fn <- build_filenames(data_path,samples)

p1 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))
p3 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.shuffled.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))

fn <- build_filenames(data_path,replicates[c(7,8,9,16,17,18)])
p2 <- plot_bw_profile(bwfiles = fn$files,labels = fn$labels,mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F) + scale_y_continuous(limits=c(0,45))

embed_plot(p1,"CAD-peaks")
embed_plot(p1,"CAD-peaks.rep")
embed_plot(p1,"CAD-ctrl")

ggarrange(p1, p2, p3, ncol = 3, nrow = 1)
```
Heatmaps of the same regions
```{r fig.width = 10, fig.height = 2}

fn <- build_filenames(data_path,samples)
p1 <- plot_bw_heatmap(bwfile = fn$files[1], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p2 <- plot_bw_heatmap(bwfile = fn$files[2], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p3 <- plot_bw_heatmap(bwfile = fn$files[3], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p4 <- plot_bw_heatmap(bwfile = fn$files[4], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p5 <- plot_bw_heatmap(bwfile = fn$files[5], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)
p6 <- plot_bw_heatmap(bwfile = fn$files[6], mode = "center",upstream = 1000, downstream = 1000, loci = "../data/pileup/GL-WT-24hr-3.pile_up3_s10_u.uniq.bed", bin_size = 10, verbose = F,zmax = 50)


ggarrange(p1, p2, p3, p4, p5, p6, ncol = 6, nrow = 1)
```

## CTCF sites - spacing  relative to nucleosomes and linker histone

around CTCF sites, the GLOE-seq signal looked very periodic, which suggested that there could be a relationship to nucleosome occupancy. Indeed, the pattern is consistent with CAD cutting left and right of linker histone / in-between linker histone and nucleosome. Note that the same periodicity is also seen in untreated and CAD KO conditions, albeit much less pronounced, suggesting that CAD-independent SSBs are also occuring more often in-between nucleosomes.

```{r  fig.width = 12, fig.height = 8, fig.show="hold", warning=F}

plus <- build_filenames_plus(data_path,samples)
minus <- build_filenames_minus(data_path,samples)

histones <- c('/Volumes/DATA/DATA/GLOE/GLOE/GEO/Torres2016_H10_1.hg19.bw','/Volumes/DATA/DATA/GLOE/GLOE/GEO/Torres2016_H14_2.hg19.bw','/Volumes/DATA/DATA/GLOE/GLOE/GEO/U2OS_AcH3_ChIPseq.hg19.bw')

p1<- plot_bw_profile(bwfiles = histones[1],labels = c("H1.0"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p2<- plot_bw_profile(bwfiles = histones[2],labels = c("H1.4"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p3<- plot_bw_profile(bwfiles = histones[3],labels = c("H3ac"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p4<- plot_bw_profile(bwfiles = c(plus$files[3],minus$files[3]),labels = c("WT 24h +","WT 24h -"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p5 <- plot_bw_profile(bwfiles = plus$files[c(1,3,6)],labels = plus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p6 <- plot_bw_profile(bwfiles = minus$files[c(1,3,6)],labels = minus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_CTCF.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

ggarrange(p1, p2, p3, p4, p5, p6, ncol = 3, nrow = 2)
```
## DNase hypersensitive sites - spacing  relative to nucleosomes and linker histone

With the removal of artifactual regions, we now also can see a neatly spaced pattern at DNAse hypersensitive sites.

```{r  fig.width = 12, fig.height = 8, fig.show="hold", warning=F}

plus <- build_filenames_plus(data_path,samples)
minus <- build_filenames_minus(data_path,samples)

histones <- c('/Volumes/DATA/DATA/GLOE/GLOE/GEO/Torres2016_H10_1.hg19.bw','/Volumes/DATA/DATA/GLOE/GLOE/GEO/Torres2016_H14_2.hg19.bw','/Volumes/DATA/DATA/GLOE/GLOE/GEO/U2OS_AcH3_ChIPseq.hg19.bw')

p1<- plot_bw_profile(bwfiles = histones[1],labels = c("H1.0"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p2<- plot_bw_profile(bwfiles = histones[2],labels = c("H1.4"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p3<- plot_bw_profile(bwfiles = histones[3],labels = c("H3ac"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p4<- plot_bw_profile(bwfiles = c(plus$files[3],minus$files[3]),labels = c("WT 24h +","WT 24h -"),mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p5 <- plot_bw_profile(bwfiles = plus$files[c(1,3,6)],labels = plus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

p6 <- plot_bw_profile(bwfiles = minus$files[c(1,3,6)],labels = minus$labels[c(1,3,6)],mode = "center",upstream = 1000, downstream = 1000, loci = "../genome/HCT116_DHS.hg19.bed", bin_size = 10, verbose = F, remove_top = 0.0001)

ggarrange(p1, p2, p3, p4, p5, p6, ncol = 3, nrow = 2)
```
